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Abstract

The goal of this paper is to detect structural damage in the presence of operational and environmental variations using
vibration-based damage identification procedures. For this purpose, four machine learning algorithms are applied based
on auto-associative neural networks, factor analysis, Mahalanobis distance, and singular value decomposition. A baseexcited
three-story frame structure was tested in laboratory environment to obtain time series data from an array of
sensors under several structural state conditions. Tests were performed with varying stiffness and mass conditions with
the assumption that these sources of variability are representative of changing operational and environmental conditions.
Damage was simulated through nonlinear effects introduced by a bumper mechanism that induces a repetitive, impacttype
nonlinearity. This mechanism intends to simulate the cracks that open and close under dynamic loads or loose
connections that rattle. The unique contribution of this study is a direct comparison of the four proposed machine
learning algorithms that have been reported as reliable approaches to separate structural conditions with changes
resulting from damage from changes caused by operational and environmental variations.

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Advanced PhotonicsJournal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews